Systems and methods for assessing the health status of a patient

ABSTRACT

Embodiments herein include medical systems, devices, and methods for assessing the health status of a patient. In an embodiment, a method includes evaluating the presence of volatile organic compounds in a breath or gas sample of the patient with a plurality of graphene sensors to generate volatile organic compound data, wherein the plurality of graphene sensors include sensors that are specific for different volatile organic compounds. The method can further include collecting data regarding the patient&#39;s sympathetic nervous activity. The method can further include combining the volatile organic compound data with the collected data regarding the patient&#39;s sympathetic nervous activity to form a combined data set. The method can further include matching the combined data set against one or more data patterns to find the best match, the best match indicating the health status of the patient. Other embodiments are also included herein.

This application claims the benefit of U.S. Provisional Application No.62/508,442, filed May 19, 2017, the content of which is hereinincorporated by reference in its entirety.

FIELD

Embodiments herein relate to medical systems, devices and methods forassessing the health status of a patient.

BACKGROUND

In the process of providing health care, clinicians often make physicalobservations and run tests to gather data about a patient. Aftercollecting data and analyzing other aspects, such as a given patient'shealth history, the clinician often forms a diagnosis and then selects atherapy to treat the diagnosed condition.

The ability of clinicians to gather data about a patient has increasedrapidly over time as devices, assays, and associated procedures haveadvanced. Yet, clinicians are still a long distance away from havingcomplete health information about each patient. As merely one issue, theability to gather data from or about a patient declines significantlywhen the patent is not in a clinical environment. Further, for mostpatients, the amount of time they spend in a clinical environment isrelatively small compared to the time spent away from clinics, thusgreatly limiting opportunities to gather data. Another issue is that notall disease states are fully characterized in terms of what pieces ofdata, that could be gathered, will provide diagnostic insight regardingthe disease state.

While clinicians may never have complete health information about eachpatient, it is possible to increase the accuracy of health assessmentsand/or diagnoses by improving the nature and quantity of data availableto clinicians.

SUMMARY

Embodiments herein include medical systems, devices and methods forassessing the health status of a patient.

In a first aspect, a method of assessing the health status of a patientis included. The method can include evaluating the presence of volatileorganic compounds in a breath or gas sample of the patient with aplurality of graphene sensors to generate volatile organic compounddata. The plurality of graphene sensors can include sensors that arespecific for different volatile organic compounds. The method canfurther include collecting data regarding the patient's sympatheticnervous activity. The method can further include combining the volatileorganic compound data with the collected data regarding the patient'ssympathetic nervous activity to form a combined data set. The method canfurther include matching the combined data set against one or morepreviously determined data patterns using a pattern matching algorithmto determine the data pattern that is the best match, wherein thespecific previously determined data pattern that is the best matchindicates the health status of the patient.

In a second aspect, in addition to or in place of other aspects herein,the one or more previously determined data patterns are created using amachine learning process.

In a third aspect, in addition to or in place of other aspects herein,the data regarding the patient's sympathetic nervous activity can beselected from the group consisting of heart rate variability (HRV),electrodermal activity (EDA), blood pressure, respiratory rate,respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS).

In a fourth aspect, in addition to or in place of other aspects herein,a method can further include collecting data regarding the patient'sfunctional status, the data selected from the group consisting of gaitand accelerometry data, and adding data regarding the patient'sfunctional status to the combined data set.

In a fifth aspect, in addition to or in place of other aspects herein, amethod can further include collecting data regarding the patient'sdemographic features and adding data regarding the patient's demographicfeatures to the combined data set.

In a sixth aspect, in addition to or in place of other aspects herein,collecting data regarding the patient's sympathetic nervous activity canbe performed in a non-clinical setting and evaluating the presence ofthe volatile organic compounds can be performed in a clinical setting.

In a seventh aspect, in addition to or in place of other aspects herein,collecting data regarding the patient's sympathetic nervous activity canbe performed with a wearable device.

In an eighth aspect, in addition to or in place of other aspects herein,collecting data regarding the patient's sympathetic nervous activity isperformed over a time period of at least about 1 day.

In a ninth aspect, in addition to or in place of other aspects herein,collecting data regarding the patient's sympathetic nervous activity isperformed with an implanted device.

In a tenth aspect, in addition to or in place of other aspects herein,the volatile organic compound data from the breath or gas sample of thepatient is downloaded from an external breath sensing system onto atleast one of a wearable device and an implantable device.

In an eleventh aspect, in addition to or in place of other aspectsherein, the collected data regarding the patient's sympathetic nervousactivity is uploaded from a wearable device to clinical diagnosticdevice.

In a twelve aspect, in addition to or in place of other aspects herein,one or more of the plurality of graphene sensors are chosen as controlson the collected data regarding the patient's sympathetic nervousactivity.

In a thirteenth aspect, in addition to or in place of other aspectsherein, the controls correlate with sympathetic nervous activity.

In a fourteenth aspect, in addition to or in place of other aspectsherein, the method can include generating a notification if the measuredvalues of the controls do not match the measured values of sympatheticnervous activity.

In a fifteenth aspect, in addition to or in place of other aspectsherein, the collected data regarding the patient's sympathetic nervousactivity reflects a baseline level of sympathetic nervous activity andchanges over the baseline level of sympathetic nervous activity.

In a sixteenth aspect, in addition to or in place of other aspectsherein, the plurality of graphene sensors can detect the presence of atleast 10 different volatile organic compounds.

In a seventeenth aspect, a diagnostic health system is included herein.The diagnostic health system can include a communications circuit, amemory circuit, and a processor in electronic communication with thecommunication circuit and the memory circuit. The processor can beconfigured to combine volatile organic compound data with collected dataregarding a patient's sympathetic nervous activity to form a combineddata set. The processor can also be configured to match the combineddata set against one or more previously determined data patterns using apattern matching algorithm to determine a pattern that is the bestmatch, wherein the specific previously determined pattern that is thebest match indicates the health status of the patient. The processor canalso be configured to report the health status of the patient based onthe best pattern match.

In an eighteenth aspect, in addition to or in place of other aspectsherein, the diagnostic health system is a wearable device and thevolatile organic compound data is downloaded onto the wearable devicefrom another device.

In a nineteenth aspect, in addition to or in place of other aspectsherein, the diagnostic health system is disposed in a clinicalenvironment and collected data regarding a patient's sympathetic nervousactivity is uploaded to the diagnostic health system from a wearabledevice.

In a twentieth aspect, a diagnostic health system is included having apatient-specific device selected from the group consisting of a wearabledevice and an implanted device. The system can also include an externalbreath sensing system and a processor receiving data from thepatient-specific device and the external breath sensing system. Thepatient-specific device can collect data regarding a patient's autonomictone, or in some embodiments, more specifically, a patient's sympatheticnervous system activity. The external breath sensing system can collectdata regarding the presence of volatile organic compounds in a breath orgas sample of the patient. The processor can be configured to combinethe volatile organic compound data with the patient's sympatheticnervous activity data to form a combined data set. The processor canalso be configured to match the combined data set against one or morepreviously determined data patterns using a pattern matching algorithmto determine a pattern that is the best match, wherein the specificpreviously determined pattern that is the best match indicates thehealth status of the patient. The processor can also be configured toreport the health status of the patient based on the best pattern match.

This summary is an overview of some of the teachings of the presentapplication and is not intended to be an exclusive or exhaustivetreatment of the present subject matter. Further details are found inthe detailed description and appended claims. Other aspects will beapparent to persons skilled in the art upon reading and understandingthe following detailed description and viewing the drawings that form apart thereof, each of which is not to be taken in a limiting sense. Thescope herein is defined by the appended claims and their legalequivalents.

BRIEF DESCRIPTION OF THE FIGURES

Aspects may be more completely understood in connection with thefollowing drawings, in which:

FIG. 1 is a schematic view of various components of a system inaccordance with various embodiments herein.

FIG. 2 is a schematic view of a patient and various devices associatedwith the patient.

FIG. 3 is a schematic cross-sectional view of an exemplarysensor/monitor device.

FIG. 4 is a schematic view of elements of a sensor/monitor device inaccordance with various embodiments herein.

FIG. 5 is a schematic cross-sectional view of an implantable medicaldevice in accordance with various embodiments herein.

FIG. 6 is a schematic view of elements of an implantable medical devicein accordance with some embodiments herein.

FIG. 7 is a schematic cross-sectional view of elements of a gas sensingdevice consistent with the technology disclosed herein.

FIG. 8 is a diagram showing correspondence of various pieces of datacollected from a gas sample along with various pieces of data indicativeof sympathetic nervous activity.

FIG. 9 is a diagram showing correspondence of various pieces of datacollected from a gas sample along with various pieces of data indicativeof sympathetic nervous activity.

While embodiments are susceptible to various modifications andalternative forms, specifics thereof have been shown by way of exampleand drawings, and will be described in detail. It should be understood,however, that the scope herein is not limited to the particularembodiments described. On the contrary, the intention is to covermodifications, equivalents, and alternatives falling within the spiritand scope herein.

DETAILED DESCRIPTION

Volatile organic compounds, as sensed in breath samples or other gassamples, can provide valuable information about the health status of apatient. In particular, patterns of volatile organic compounds(including the presence, absence, and/or concentration of a plurality ofdifferent volatile organic compounds) in a breath or gas sample of apatient can be associated with various disease states and/or particularhealth statuses.

In some cases, though, the predictive power of a pattern of volatileorganic compounds standing alone may have less than a desired level ofaccuracy. However, factors outside of the patient's breath or other gassample can be leveraged to make the diagnostic more specific andsensitive.

Data regarding a patient's autonomic tone, or in some embodiments morespecifically, a patient's sympathetic nervous system activity, can beused in combination with data regarding volatile organic compounds toenhance diagnostic specificity and/or sensitivity. For example, theonset of many illnesses are accompanied by increases in the body'ssympathetic nerve activity. These changes can be detected directly orindirectly by using a number of physiological signals, including heartrate variability (HRV), electrodermal activity (EDA), factors related tothe effect of sympathetic tone on vessel constriction (including, butnot limited to, blood flow, perfusion, skin temperature, bodytemperature, blood pressure, and the like), respiratory rate,respiratory sinus arrhythmia (RSA), baroreceptor sensitivity (BRS),pupil diameter, and electrooculography. It will be appreciated thatmeasurement of autonomic tone (e.g., parasympathetic/sympatheticbalance) or measurement of parasympathetic tone can also be performed inaddition to or instead of measurement of sympathetic nervous activity insome embodiments. Functional signals such as gait and accelerometry canalso reveal small changes in health status. Many of these signals can beeasily measured in an acute setting, such as when the patient isperforming the breath sensor test in the clinic. However, in some casesthese signals are recorded chronically, either before or after thebreath or gas sensor test. In this way, the clinician is able to obtaina mean signal. Further details of data that can be gathered and/or usedin accordance with embodiments herein are described below.

Data regarding the patient's sympathetic nervous activity can becollected over various time periods. In some embodiments, such data iscollected over a time period of at least about 1 second, 5 seconds, 10seconds, 30 seconds, 1 minute, 5 minutes, 15 minutes, 30 minutes, 45minutes, 60 minutes, 2 hours, 4 hours, 8 hours, 12 hours, 24 hours, 3days, 5 days, 7 days, 9 days, 11 days, 13 days, 15 days, 30 days, 45days, 60 days, 90 days, or 120 days. In some embodiments, the patient'ssympathetic nervous activity can be collected over a time period in arange wherein any of the foregoing amounts of time can serve as theupper or lower bound of the range, provided that the upper bound isgreater than the lower bound.

Thus, in accordance with various embodiments herein, other types of datacan combined with data regarding volatile organic compounds in order toimprove the overall accuracy of assessments of the patient's healthstatus. In particular, data regarding the sympathetic nervous state of apatient can be combined with volatile organic compound data in order toimprove the accuracy of assessments of the patient's health statusand/or disease state. For example, such combined data can be used todetect the signature of a medical condition. The medical condition canbe any disease state including, but not limited to, lung cancer, coloncancer, pulmonary disease (e.g. asthma, COPD), cardiovascular disease(e.g. heart failure), digestive and inflammatory diseases (e.g.inflammatory bowel diseases such as Crohn's, colitis, or the like) ordiabetes.

Volatile organic data can be gathered in various settings includingnon-clinical settings and clinical settings. Similarly, data regardingthe sympathetic nervous state of a patient can be gathered in varioussettings including non-clinical settings and clinical settings.

In some cases, data gathered in a non-clinical setting can be combinedwith data gathered in a clinical setting. For example, data gathered ina clinical setting (such as breath or gas analysis data) can bedownloaded to a wearable or implantable device where further operationsrelying upon the downloaded data can be executed by the wearable orimplantable device. However, in other examples, data gathered in anon-clinical setting (such as data regarding the sympathetic nervousstate of a patient) can be uploaded to a device (testing device, systemor computer) in a clinical setting and then further operations relyingupon the uploaded data can be executed by the device in the clinicalsetting.

Referring now to FIG. 1, a schematic view is shown of possiblecomponents of a system 100 in accordance with various embodimentsherein. The system 100 can include external patient specific deviceswithin a non-clinical environment 104 (or ambulatory setting) including,but not limited to, a smart phone 106, a wearable device 108, and apatient-specific data gathering device 112, such as a weight scale. Thenon-clinical environment 104 can also include devices implanted withinthe patient 102 (discussed in greater detail with respect to FIGS. 2-3below).

The non-clinical environment 104 can also include a patient communicator110 (or patient management device). An exemplary patient managementsystem is the LATITUDE® patient management system, commerciallyavailable from Boston Scientific Corporation, Natick, Mass. Aspects ofan exemplary patient management system are described in U.S. Pat. No.6,978,182, the content of which is herein incorporated by reference.

The system 100 can also include devices within a clinical environment120 (or non-ambulatory setting) including, but not limited to, aprogrammer device 124 that can be used to send data to and/or receivedata from implanted devices as well as from other devices across anetwork.

The clinical environment 120 can also include a breath sensing system160 for sensing gaseous analytes (or volatile organic compounds) inaccordance with various embodiments herein. In this embodiment, thesystem is in a hand-held format. It will be appreciated, however, thatmany other formats for the system are contemplated herein.

The breath sensing system 160 can include a housing 178. The system 160can include a mouthpiece 162 into which a subject to be evaluated canblow a breath sample. The system 160 can also include a display screen174 and a user input device 176, such as a keyboard. The system can alsoinclude a gas outflow port 172. Aspects of breath sensing systems aredescribed in U.S. Publ. Appl. No. 2016/0109440, the content of which isherein incorporated by reference. While FIG. 1 shows a breath sensingsystem, it will be appreciated that other types of gas sampling systemscan also be used herein. For example, gas sampling devices for use withcatheters and endoscopy systems can also be used. An exemplary gassampling system in the context of a catheter or endoscopy device isdescribed in U.S. Appl. No. 62/350,345, the content of which is hereinincorporated by reference.

Devices and systems in the clinical environment 120 can communicate withdevices and systems in the non-clinical environment 104 for the exchangeof data. Devices and systems in both the clinical environment 120 andthe non-clinical environment 104 can also communicate with computingdevices in remote locations through a data network 140, such as theInternet or another network for the exchange of data as packets, frames,or otherwise.

In some embodiments, the system 100 can also include a computing devicesuch as a server 130 (real or virtual). In some embodiments, the server130 can be located remotely from the non-clinical environment 104 and/orthe clinical environment 120. The server 130 can be in datacommunication with a database 132.

The database 132 can be used to store various patient information, suchas that described herein. In some embodiments, the database canspecifically include an electronic medical database containing dataregarding the health status of a patient, patterns of data associatedwith various conditions (such as that generated from machine learninganalysis of large sets of patient data), demographic data and the like.

The server 130 can be in data communication with the non-clinicalenvironment 104 and/or the clinical environment 120 through a networksuch as the Internet or another public or private data network includingpacket switched data networks or non-packet switched data networks. Insome embodiments, the server 130 can be located in proximity tonon-clinical environment 104 and/or the clinical environment 120.

As described above, FIG. 1 shows devices in a non-clinical environment104 as well as a clinical environment 120. However, it will beappreciated that some devices shown in the non-clinical environment canalso be present in and used in a clinical environment. Similarly, somedevices shown in the clinical environment can be present in and used ina non-clinical environment. In addition, some systems herein do notinclude all of the various elements shown in FIG. 1. Also, in somecases, systems herein can include additional components not shown inFIG. 1.

Referring now to FIG. 2, a schematic view is shown of patient 102 andvarious devices that can be associated with a patient 102. The patient102 can have various implanted devices and/or various external devices.In specific, the patient 102 can utilize a wearable device 108. Whilethe wearable device 108 in FIG. 2 is on the patient's 102 wrist, it willbe appreciated that this is merely one example and the device can alsobe worn on other parts of the patient 102. The wearable or otherexternal devices can provide various functionality. In some embodiments,the wearable device(s) can include sensors, such as any of the types ofsensors described herein. The wearable device(s) can specifically beused to gather data regarding the sympathetic nervous state of a patient(of subject).

In some embodiments, the wearable or other external device can be usedto provide alerts to the patient and/or to care providers located in thesame place as the patient or remotely. Alerts can take various forms. Insome embodiments, the alert can be an audio and/or visual alert. In someembodiments, the wearable or other external device can be used todisplay information to the patient and/or to care providers. In someembodiments, the wearable or other external devices can be used toprovide a prompt to the patient in order to get them to take some actionin order to gather data.

Beyond external devices, there may also be implanted devices associatedwith the patient to gather data. For example, in some embodiments thepatient 102 can have an implanted cardiac device 204. In someembodiments, the implanted cardiac device 204 can be connected to leadsfor sensing and/or electrical stimulation that can be disposed in ornear the patient's heart 202. The implanted cardiac device 204 caninclude various sensors and/or can be connected to various sensors.

In some embodiments, an implanted monitoring/sensing device 206 can beimplanted within the patient 102. Further details of an exemplaryimplanted monitoring/sensing device 206 are provided below with respectto FIG. 3 and the accompanying description. However, it will beappreciated that there are many different types of implanted devicesthat can be used with systems herein.

Embodiments of systems herein can include sensor/monitor devices.Referring now to FIG. 3, a schematic cross-sectional view of anexemplary sensor/monitor device 300 is shown in accordance with variousembodiments herein. The sensor/monitor device 300 includes a housing304. The housing 304 of the sensor/monitor device 300 can includevarious materials such as metals, polymers, ceramics, and the like. Insome embodiments, the housing 304 can be a single integrated unit. Inother embodiments, the housing 304 can include a main segment 313 alongwith appendage segments 315 and 317. In one embodiment, the housing 304,or one or more portions thereof, is formed of titanium. In someembodiments, one or more segments of the housing 304 can be hermeticallysealed. In some embodiments, the main segment 313 is formed of a metaland the appendage segments 315 and 317 are formed from a polymericmaterial.

The housing 304 defines an interior volume 370 that in some embodimentsis hermetically sealed off from the area 372 outside of thesensor/monitor device 300. The sensor/monitor device 300 can includecircuitry 351. The circuitry 351 can include various components, such ascomponents 390, 392, 394, 396, 398, and 399. In some embodiments, thesecomponents can be integrated, and in other embodiments, these componentscan be separate. In some embodiments, the components can include one ormore of a microprocessor, memory circuitry (such as random access memory(RAM) and/or read only memory (ROM)), recorder circuitry, telemetrycircuitry, sensor and/or sensor interface circuitry, power supplycircuitry (which can include one or more batteries), normalizationcircuitry, control circuitry, evaluation circuitry, and the like. Insome embodiments, recorder circuitry can record the data produced by thevarious sensors and record time stamps regarding the same. In someembodiments, the circuitry can be hardwired to execute various functionswhile in other embodiments, the circuitry can be implemented asinstructions executing on a microprocessor or other computation device.

The sensor/monitor device 300 can include, for example, an electricalfield sensor that is configured to generate a signal corresponding tocardiac electric fields. The electrical field sensor can include a firstelectrode 382 and a second electrode 384. In some embodiments, thehousing 304 itself can serve as an electrode. The electrodes can be incommunication with the electrical field sensor. The electrical fieldsensor can include a circuit in order to measure the electricalpotential difference (voltage) between the first electrode 382 and thesecond electrode 384. The electrical field sensor can include a circuitin order to measure the impedance between the first electrode 382 andthe second electrode 384. The sensor/monitor device 300 can also includean antenna 380, to allow for unidirectional or bidirectional wirelessdata communication.

In some embodiments, the sensor/monitor device 300 can also include achemical sensor 306. In the embodiment shown in FIG. 3, the chemicalsensor can be an optical chemical sensor. However, in other embodiments,the chemical sensor can be a potentiometric chemical sensor. Thechemical sensor 306 can specifically include a chemical sensing element322, an optical window 344, and an electro-optical module 328. Theelectro-optical module 328 can be in electrical communication with thecircuitry 351 within the interior volume 370, and in some embodiments,the circuitry 351 is configured to selectively activate the chemicalsensor 306. The chemical sensor 306 can be configured to be chronicallyimplanted.

The chemical sensor 306 can include an electro-optical module 328coupled to the optical window 344. The electro-optical module 328 canspecifically include one or more optical excitation assemblies. Eachoptical excitation assembly can include various light sources such aslight-emitting diodes (LEDs), vertical-cavity surface-emitting lasers(VCSELs), electroluminescent (EL) devices, or the like. Theelectro-optical module 328 can also include one or more opticaldetection assemblies. Each optical detection assembly can include one ormore photodiodes, avalanche photodiodes, a photodiode array, a phototransistor, a multi-element photo sensor, a complementary metal oxidesemiconductor (CMOS) photo sensor, or the like.

The chemical sensing element 322 can be disposed on or over the opticalwindow 344. The chemical sensing element 322 can be configured to detecta physiological analyte by exhibiting an optically detectable responseto the physiological analyte. Specific examples of physiologicalanalytes are discussed in greater detail below. In operation, analytesof interest from the in vivo environment can diffuse into the chemicalsensing element 322 causing a detectable change in the opticalproperties of the chemical sensing element 322. Light can be generatedby the electro-optical module 328 and can pass through the opticalwindow 344 and into the chemical sensing element 322. Light can theneither be preferentially reflected from or re-emitted by the chemicalsensing element 322 proportional to the sensed analyte and pass backthrough the optical window 344 before being received by theelectro-optical module 328. Various aspects of exemplary chemicalsensors are described in greater detail in U.S. Pat. No. 7,809,441, thecontent of which is herein incorporated by reference in its entirety.

In some embodiments the chemical sensing element 322 is located in afluid such as blood, interstitial fluid, urine, lymph or chyle andsenses analytes in the fluid. In other embodiments, the chemical sensingelement 322 is located in a solid tissue such as muscle, fat, bone, bonemarrow, organ tissues (e.g. kidney, liver, brain, lung, etc.) and sensesanalytes in the solid tissue.

Elements of various devices (such as external wearable devices and/orimplanted devices) that can be used as part of systems herein are shownin FIG. 4. However, it will be appreciated that some embodiments devicesused herein with systems can include additional elements beyond thoseshown in FIG. 4. In addition, some embodiments of devices used withsystems herein may lack some elements shown in FIG. 4. The device 400(which can be implanted or external) can gather information through oneor more sensing channels. A microprocessor 410 can communicate with amemory 412 via a bidirectional data bus. The memory 412 can include readonly memory (ROM) or random access memory (RAM) for program storage andRAM for data storage.

The device 400 can include one or more electric field sensors 422 (insome cases, electrodes) and an electric field sensor channel interface420 (for measuring impedance, electrical potential, or other electricalproperties) which can communicate with a port of microprocessor 410. Thedevice 400 can also include one or more other sensor(s) 432 and othersensor channel interface 430 which can communicate with a port ofmicroprocessor 410.

The other sensors (implantable, wearable, or non-wearable external) caninclude, but are not limited to, one or more of a motion sensor, aposture sensor, an activity sensor, a respiration sensor, a pressuresensor (including blood pressure and/or urine pressure), flow sensor,impedance sensor, and any of the other types of sensors discussedherein.

The device 400 can also include a chemical sensor 438 and a chemicalsensor channel interface 436 which can communicate with a port ofmicroprocessor 410. The sensor channel interfaces 420, 430 and 436 caninclude various components such as analog-to-digital converters fordigitizing signal inputs, sensing amplifiers, registers which can bewritten to by the control circuitry in order to adjust the gain andthreshold values for the sensing amplifiers, and the like. A telemetryinterface (or telemetry circuit) 440 is also provided for communicatingwith other devices of a system such as a programmer, a home-based unitand/or a mobile unit (e.g., a cellular phone).

Data herein can also be gathered by various other types of implantablemedical devices, including but not limited to implantable cardiacdevices. Referring now to FIG. 5, a schematic cross-sectional view of animplantable medical device 500 is shown in accordance with variousembodiments herein. The implantable medical device 500 includes a headerassembly 502 and a housing 504. The housing 504 of the implantablemedical device 500 can include various materials such as metals,polymers, ceramics, and the like. In one embodiment, the housing 504 isformed of titanium. The header assembly 502 can be coupled to one ormore electrical stimulation leads 550. The header assembly 502 serves toprovide fixation of the proximal end of one or more leads andelectrically couples the leads to components within the housing 504. Theheader assembly 502 can be formed of various materials including metals,polymers, ceramics, and the like.

The housing 504 defines an interior volume 570 that is hermeticallysealed off from the volume 572 outside of the device 500. Variouselectrical conductors 509, 511 can pass from the header 502 through afeed-through structure 505, and into the interior volume 570. As such,the conductors 509, 511 can serve to provide electrical communicationbetween the electrical stimulation lead 550 and control circuitry 551disposed within the interior volume 570 of the housing 504. The controlcircuitry 551 can include various components such as a microprocessor,memory (or memory circuit) (such as random access memory (RAM) and/orread only memory (ROM)), a telemetry module, electrical field sensor andstimulation circuitry, a power supply (such as a battery), and anoptical sensor interface channel, amongst others. The control circuitry551 can include the evaluation circuitry in various embodiments herein.

The implantable medical device 500 can incorporate, for example, anelectrical field sensor that is configured to generate a signalcorresponding to cardiac electric fields. The electrical field sensor(for measuring impedance, electrical potential, or other electricalproperties) can include a first electrode and a second electrode. Theelectrodes of the electrical field sensor can be the same electrodesused to provide electrical stimulation or can be different electrodes.In some embodiments, one or more electrodes can be mounted on one ormore electrical stimulation leads 550. In some embodiments, the housing504 can serve as an electrode. The electrodes can be in communicationwith the electrical field sensor and stimulation circuitry. Theelectrical field sensor can include a circuit (such as within controlcircuitry 551) in order to measure the electrical potential difference(voltage) between the first electrode and the second electrode. In someembodiments, the data from the electrical field sensor can be used togenerate an electrocardiogram.

The implantable medical device 500 can also include a chemical sensor506. In the embodiment shown in FIG. 5, the chemical sensor 506 is apotentiometric chemical sensor. The chemical sensor 506 can specificallyinclude a receptor module 522, and a transducer module 528. Thetransducer module 528 can be in electrical communication with thecircuitry 551 within the interior volume 570, and in some embodiments,the control circuitry 551 is configured to selectively activate thechemical sensor 506. The chemical sensor 506 can be configured to bechronically implanted.

The chemical sensor 506 can be configured to detect a physiologicalanalyte by exhibiting an electrical signal response to the physiologicalanalyte. In operation, analytes of interest from the in vivo environmentcan contact the receptor module 522 causing a detectable change in theelectrical properties of the same. The transducer module 528 can then beused to process and/or propagate the signal created by the receptormodule 522. While medical device 500 is described as being implantable,it will be appreciated that some or all of the same components andfunctionality can be included in an external and/or wearable medicaldevice.

Elements of some embodiments of an implantable medical device that canbe part of systems herein are shown in FIG. 6. However, it will beappreciated that some embodiments can include additional elements beyondthose shown in FIG. 6. In addition, some embodiments may lack someelements shown in FIG. 6. The medical device 600 can sense cardiacevents through one or more sensing channels and can output pacing pulsesto the heart via one or more pacing channels in accordance with aprogrammed pacing mode. A microprocessor 610 communicates with a memory612 via a bidirectional data bus. The memory 612 typically comprisesread only memory (ROM) or random access memory (RAM) for program storageand RAM for data storage.

The implantable medical device can include atrial sensing and pacingchannels comprising at least a first electrode 634, a lead 633, asensing amplifier 631, an output circuit to provide a stimulus 632, andan atrial channel interface 630 which can communicate bidirectionallywith a port of microprocessor 610. In this embodiment, the device 600also has ventricular sensing and pacing channels comprising at least asecond electrode 624, a lead 623, a sensing amplifier 621, an outputcircuit to provide a stimulus 622, and ventricular channel interface620. For each channel, the same lead and electrode are used for bothsensing and pacing. The channel interfaces 620 and 630 includeanalog-to-digital converters for digitizing sensing signal inputs fromthe sensing amplifiers and registers which can be written to by thecontrol circuitry in order to output pacing pulses, change the pacingpulse amplitude, and adjust the gain and threshold values for thesensing amplifiers. The implantable medical device 600 can also includea chemical sensor 638 and a chemical sensor channel interface 636. Atelemetry interface 640 is also provided for communicating with anexternal programmer or another implanted medical device.

Systems herein can also include a breath and/or gas sensing device orsystem. In particular, systems herein can gather data on the presence,absence, and/or amount of various gaseous analytes including, but notlimited to, volatile organic compounds. FIG. 7 is a schematiccross-sectional view of an example system 700 consistent with thetechnology disclosed herein. It will be appreciated that this schematicview has been simplified for ease of illustration and that embodimentsof systems and devices herein can include various features not shown inFIG. 7. In addition, some embodiments of systems and devices herein maylack various features shown in FIG. 7. The system 700 is generallyconfigured for collecting a gas sample and communicating data associatedwith the gas sample. The system 700 has a gas sampling device 710 and adocking station 730.

The gas sampling device 710 can be configured to collect a gas sampleand facilitate testing of the gas sample to generate data. In someembodiments, the gas sampling device 710 can be configured as a handhelddevice. In such cases, the gas sampling device can be configured to beheld in the hand of a care provider, a patient, or both, during certainsteps of its use, while also being configured to be held or otherwisepositioned in association with the docking station 730 during certainsteps of its use.

In some embodiments, the gas sampling device 710 is configured toreceive a gas sample, such as exhaled breath, from a patient and directthe gas sample to a testing location. The gas sampling device 710generally has a housing 720 defining an airflow aperture 722, a gastesting chamber 726, a sensor receptacle 728, an airflow pathway 724,and a docking structure 721.

When receiving a gas sample, the gas (such as breath from a patient),can pass into the gas sampling device 710 through the airflow aperture722, through the airflow pathway 724, into the gas testing chamber 726and into contact with one or more measurement zones 742 of a disposablesensor test strip 740, and then out the end of the gas testing chamber726 through the sensor receptacle 728, or through a separate exhaustport (not shown in this view). While this view depicts contact betweenthe sensor receptacle 728 and the disposable sensor test strip 740, itwill appreciated that there can be segments or areas where the sensorreceptacle 728 and the disposable sensor test strip 740 do not contactor do not create sealing contact, thus allowing for a path for the gasto flow out through the sensor receptacle 728.

While FIG. 7 shows the airflow pathway 724 to be approximately the samesize as the interior space of the housing 720, it will be appreciatedthat this is simply for ease of illustration and that the size of theairflow pathway 724 can be, in many cases, much smaller than the entireinterior size of the housing 720, allowing for room for other componentswithin the interior of the housing 720, such as other componentsdescribed herein including, but not limited to, sensors, a power source,processing devices, communication hardware, conditioning elements, andthe like.

The housing 720 can be constructed of a variety of materials andcombinations of materials. The housing 720 can be a single cohesivestructure or can be constructed of multiple components that are coupledto form the housing 720. As an illustrative example, a portion of thehousing 720 that defines the airflow pathway 724 can be coupled to theportion of the housing 720 that defines the airflow aperture 722. Theportion of the housing 720 that defines the airflow pathway 724 caninclude a conduit or tube with various different cross-sectional sizesand shapes. The conduit or tube can be formed from various materialsincluding, but not limited to, polymers, metals, ceramics, glass,composites or the like. In some embodiments, surfaces lining the airflowpathway 724 can be coated with materials to provide various desirablefunctional properties.

The airflow aperture 722 is generally configured to provide an input forthe gas sample at the housing 720. In some embodiments the airflowaperture 722 is configured to be in fluid communication with a patient'smouth, although in some other embodiments a protective liner can be usedto provide a barrier between the patient's mouth and the housing, whichwill be described in more detail, below.

The airflow pathway 724 generally is configured to direct the gas inputat the airflow aperture 722 to the gas testing chamber 726. As such, theairflow pathway 724 generally extends from the airflow aperture 722 tothe gas testing chamber 726. The airflow pathway 724 can have across-sectional area that is substantially the same along the length ofthe airflow pathway or it can vary. In some embodiments, the gas testingchamber 726 can have different interior dimensions (e.g., height, width,etc.) than the airflow pathway leading to it.

The gas testing chamber 726 defines a testing location for the gassample. In various embodiments, the gas testing chamber 726 isconfigured to receive a measurement zone 742 of a disposable sensor teststrip 740. Accordingly, the sensor receptacle 728 defined by the housing720 is generally configured to removably retain the disposable sensortest strip 740 within the gas testing chamber 726. In variousembodiments the sensor receptacle 728 is configured to slidably receivethe disposable sensor test strip 740 that is manually inserted by auser. In some embodiments, the disposable sensor test strip 740 can beinserted with its long (or major) axis parallel to the long (or major)axis of the housing 720. However, in other embodiments, the disposablesensor test strip 740 can be inserted with its long (or major) axispositioned differently with respect to the long (or major) axis of thehousing 720, such as perpendicular. Example sensor test strips will bedescribed in more detail, below.

While FIG. 7 depicts the test strip located approximately in the middleof the gas sampling device 710 (top to bottom with regard to theperspective of the figure), it will be appreciated that the test stripcan be positioned biased toward the top or the bottom, to be closer toan exterior surface of the housing 720 or gas sampling device 710. Insome cases this can facilitate easier wireless reading of the disposablesensor strip by the docking station while the disposable sensor strip isstill held within the housing. In some embodiments, the disposablesensor strip can be positioned less than 5 cm, 4 cm, 3 cm, 2 cm, 1 cm,0.5 cm, 0.2 cm or less from exterior surface (or exterior wall) of thehousing 720.

The docking station 730 is generally configured to collect datagenerated from testing the gas sample. The docking station 730 has areading device 732 having communication hardware to wirelessly receivedata through the housing of the gas sampling device 710. In manyembodiments the reading device 732 of the docking station 730 isconfigured to wirelessly receive data from the disposable sensor teststrip 740. In various embodiments, the reading device 732 can also beconfigured to wirelessly receive baseline data through the housing ofthe gas sampling device 710—from the disposable sensor test strip740—where the term “baseline data” is defined as data collected beforeexposure of the disposable sensor test strip 740 to the gas sample orthe patient or test subject. In some cases the baseline data can reflectconditions of whatever gas happens to be in the testing chamber prior toobtaining a gas sample of a patient. However, in other embodiments,ambient air can purposefully be pushed through the testing chamber,and/or a particular reference gas sample of known composition can be putinto the testing chamber for purposes of generating baseline data. Thecommunication hardware of the reading device 732 can be capable of nearfield communication with the disposable sensor test strip 740. In someembodiments the communication hardware of the reading device 732 is anear field electrode or near field reading circuit that is configured toreceive patient data from a passive electrical circuit, such as bydetecting a resonant frequency of an LRC resonator circuit and/orchanges to the same.

In some embodiments the docking station has a proximity sensor that isconfigured to detect when the gas sampling device 710 is in sufficientproximity to the docking station 730 to collect data. And, although notcurrently depicted, in some embodiments the disposable sensor test strip740 can have identifying information disposed thereon, other than thebaseline or patient sample data, that can be read by a docking stationor another device such as an identification code, radio frequencyidentification (RFID) tag, barcode, serial or id numbers, or otherindicia. In such embodiments the docking station 730 (FIG. 7) can beconfigured to read, collect, save, and/or potentially transmit thatidentification data.

The docking station 730 is generally configured to be a docking locationfor the gas sampling device 710. The docking station 730 is generallyconfigured to physically receive the gas sampling device 710. Thedocking station 730 can receive the gas sampling device 710 through avariety of structures and configurations that will be appreciated bythose having ordinary skill in the art. In various embodiments thedocking station 730 and the docking structure 721 of the gas samplingdevice 710 have a mating configuration by which the docking station 730receives the docking structure 721 of the gas sampling device 710. Insome such embodiments the docking station 730 and the docking structure721 define an interference fit. However, in other embodiments, thedocking station 730 can simply rest upon or in the docking structure721. In some embodiments the docking station 730 and the dockingstructure 721 are configured to position the disposable sensor teststrip 740 and the reading device 732 in sufficient proximity toaccommodate transmission of data between the reading device 732 anddisposable sensor test strip 740. In some embodiments the dockingstation and the docking structure are configured to position thedisposable sensor test strip 740 and the reading device 732 within 6 cm,5 cm, 4 cm, 3 cm, or 2 cm of each other, or even within 1 cm of eachother.

The docking station 730 can have various additional components. In someembodiments the docking station 730 has a processor 736 and memory 735.The processor 736 and memory 735 can be configured to process and storedata obtained from tested the gas sample. For example, the memory 735can store baseline data locally and the processor 736 can be configuredto remove collected baseline data from the tested gas data to obtainadjusted data. Such adjusted data can remove some impact of the ambientenvironment on the tested gas data. In another example, the processorcan be configured to compare the adjusted data (or, in some embodimentsthe tested gas data) to known data indicative of one or more diseases.Such a comparison can be used to identify the presence of a particulardisease using a comparative algorithm. In yet another example, theprocessor of the docking station 730 can be configured to identify adefect in the disposable sensor test strip 740. Example defects caninclude manufacturing defects and/or premature exposure to ambientgases. The docking station 730 can be configured to collect, save, andpotentially transmit records of such defects.

The docking station 730 has networking hardware 734 in variousembodiments. The networking hardware 734 can be configured to transmitdata over a network to a remote system, including a cloud-based system.In some implementations the remote system can be a hospital, clinic,laboratory, or other location. In some embodiments the networkinghardware 734 is configured to transmit data generated from testing thegas sample. The networking hardware 734 is configured to transmitbaseline data in some embodiments. The networking hardware is configuredto transmit adjusted data in some embodiments. In some embodiments theremote system analyzes the data it receives. For example, in someembodiments the remote system is configured to compare the adjusted datato known data indicative of a plurality of diseases. That comparison canidentify the presence of a particular disease.

In some embodiments the docking station 730 has a user interface 738.The user interface 738 can be configured to communicate information to auser. For example, the user interface 738 can be configured tocommunicate an active data transmission, such as a data transmissionbetween the docking station 730 and the gas sampling device 710 and/orbetween the docking station 730 and a network. In some embodiments theuser interface 738 can be configured to communicate information aboutthe current stage of the testing process, progress of the same, or whatsteps are next or what actions are required. For example, in some casesthe user interface 738 can be configured to communicate that that thegas sampling device 710 is ready to receive a gas sample or that thedocking station 730 has finished reading data from the gas samplingdevice 710. The user interface 738 can also be configured to communicatea defect in the sensor test strip. The user interface 738 can beconfigured to communicate through visual notification, audionotification, and the like. As a specific example, a flashing light canbe used to indicate that the docking station 730 is transmitting data.The user interface 738 can include a light source such as an LED orsimilar light emitting device.

One example approach to using the system depicted in FIG. 7 will now bedescribed. A disposable sensor test strip 740 is inserted into the gassampling device 710 such that it is received by the gas testing chamber726 defined by a housing of a gas sampling device. The gas samplingdevice 710 having the disposable sensor test strip 740 is docked to thedocking station 730, and the reading device 732 of the docking station730 reads baseline data from the disposable sensor test strip 740through the housing 720 of the gas sampling device 710. The gas samplingdevice 710 is undocked from the docking station 730 after reading thebaseline data, and a gas sample is received by the gas testing chambersuch that the gas sample is brought into contact with the disposablesensor test strip 740. For example, the gas sampling device 710 may bephysically grasped by a care provider and removed from the dockingstation 730 and physically handed to a patient or test subject who maythen blow into the gas sampling device 710 to provide the gas sample tobe analyzed. In other cases, the gas sampling device 710 may be held bythe care provider instead of being held by the patient or test subject.The gas sampling device 710 can then be docked to the docking station730 after receiving the gas sample, and the data from the tested gas isread from the disposable sensor test strip 740 by the reading device732, wherein the adjusted data is read through the housing 720 of thegas sampling device 710. In various embodiments the disposable sensortest strip 740 is configured to be single-use. As such, the disposablesensor test strip 740 can be disposed of following the collection ofsample gas data from the disposable sensor test strip 740. Various othermethods of using the system depicted in FIG. 7 are also contemplated.

The measurement zones 742 can include a plurality of discrete bindingdetectors that can include one or more analyte binding receptors boundthereto. In some embodiments, all of the analyte binding receptorswithin a particular discrete binding detector can be the same withrespect to their analyte binding properties. In other embodiments, atleast some of the analyte binding receptors within a particular zone canbe different from one another with respect to their analyte bindingproperties. In some embodiments, each discrete binding detector can beunique. In some embodiments, discrete binding detectors that are uniquecan be cross-reactive in that they bind to different portions ordifferent configurations of the same chemical compound. In someembodiments, each discrete binding detector can include a single passivesensor circuit. In other embodiments, each discrete binding detector caninclude multiple passive sensor circuits.

In some embodiments, the passive sensor circuit can include a graphenevaractor (variable capacitor) or metal-graphene-oxide capacitor (whereinRS represents the series resistance and CG represents the varactorcapacitor) coupled to an inductor. Graphene varactors can be prepared invarious ways and with various geometries. As just one example, in someaspects, a gate electrode can be recessed into an insulator layer. Agate electrode can be formed by etching a depression into the insulatorlayer and then depositing an electrically conductive material in thedepression to form the gate electrode. A dielectric layer can be formedon a surface of the insulator layer and the gate electrode. In someexamples, the dielectric layer can be formed of a material, such as,aluminum oxide, hafnium dioxide, zirconium dioxide, hafnium silicate orzirconium silicate. A graphene layer can be disposed on the dielectriclayer. In some aspects, the graphene layer can be a graphene monolayer.Contact electrodes can also be disposed on a surface of the graphenelayer. Aspects of exemplary graphene varactors can be found in U.S.Publ. App. No. 2014/0145735, the content of which is herein incorporatedby reference.

In various embodiments, the functionalized graphene layer (e.g.,functionalized to include analyte binding receptors), which is part ofthe graphene varactor and thus part of a sensor circuit such as apassive sensor circuit, is exposed to the gas sample flowing over thesurface of the measurement zone. The passive sensor circuit can alsoinclude an inductor. In some embodiments, only a single varactor isinclude with each passive sensor circuit. In other embodiments, multiplevaractors are included, such as in parallel, with each passive sensorcircuit.

In the passive sensor circuit, the quantum capacitance of the electricalcircuit changes upon binding between the analyte binding receptors and acomponent from a gas sample. The passive sensor circuit can function asan LRC resonator circuit, wherein the resonant frequency of the LRCresonator circuit changes upon binding with a component from a gassample.

The reading circuit can be used to detect the electrical properties ofthe sensor circuit. By way of example, the reading circuit can be usedto detect the resonant frequency of the LRC resonator circuit and/orchanges in the same. In some embodiments, the reading circuit caninclude a reading coil having a resistance and an inductance. When thesensor-side LRC circuit is at its resonant frequency, a plot of thephase of the impedance of the reading circuit versus the frequency has aminimum (or phase dip frequency). Sensing can occur when the varactorcapacitance varies in response to binding of analytes, which changes theresonant frequency, and the value of the phase dip frequency. Othertechniques of reading graphene sensors can also be used.

Further aspects of gas and/or breath sampling systems are described inU.S. Publ. Appl. No. 2016/0109440, the content of which is hereinincorporated by reference.

In some cases, the individual pieces of data gathered may be independentand distinct from one another. In other cases, some individual pieces ofdata can be associated with and/or correlated with other pieces of dataand used for various purposes including, but not limited to, controlsand/or validation data.

Referring now to FIG. 8, a schematic representation is shown of variouspieces of data that can be combined to form a combined data set for usein later operations such as machining learning analysis and/or patternmatching. In FIG. 8, the combined data can include volatile organiccompound data 802, such as that which can be generated using gas orbreath sampling devices as described herein. The volatile organiccompound data 802 can include a plurality of individual pieces 812 ofdata. The combined data can also include data regarding the patient'ssympathetic nervous activity 804. It will be appreciated that in someembodiments the combined data can include any of the types of datadescribed herein. In some embodiments, the data regarding the patient'ssympathetic nervous activity 804 can include a plurality of individualpieces of data 814. In this schematic view, all of the individual piecesof data (812 and 814) are independent and distinct.

Referring now to FIG. 9, another schematic representation is shown ofvarious pieces of data that can be combined to form a combined data setfor use in later operations such as machining learning analysis and/orpattern matching. In FIG. 9, the combined data can include volatileorganic compound data 802, such as that which can be generated using gasor breath sampling devices as described herein. The volatile organiccompound data 802 can include individual pieces of data 812 broken upinto a first set of data 902 that is independent and distinct from otherpieces of data and a second set of data 904 that is related to orcorrelates to certain other pieces of data, such as data regarding thepatient's sympathetic nervous activity 804, or other types of data. Inthis manner, the first set of data 902 can be used in various ways suchas a control or otherwise for validation purposes before the data iscombined.

Sensors and Data

In various embodiments herein, the patient-specific device and/or otherdevices or systems that may be part of a system can collect dataregarding a patient's autonomic tone, or in some embodiments morespecifically, a patient's sympathetic nervous system activity. However,it will be appreciated that measurement of autonomic tone (e.g.,parasympathetic/sympathetic balance) or measurement of parasympathetictone can also be performed in addition to or instead of measurement ofsympathetic nervous activity in some embodiments. In some embodimentsother types of data can also be included such as demographic data,medical record data, measurements of environmental conditions, patientactivity data, indications of symptoms, information regarding thecurrent or past physical state of the patient, and the like.

Many different measures of sympathetic nervous activity can be gatheredand/or evaluated. In some embodiments, measures of sympathetic nervousactivity herein can include one or more of heart rate variability (HRV),electrodermal activity (EDA), blood pressure, respiratory rate,respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS).

Many different specific sensors can be used to gather data that reflectssympathetic nervous activity. In some embodiments, an ECG sensor can beused. The ECG sensor can include at least two electrodes disposed in thepatient's body configured to detect electrical activity from thepatient's body. A processor circuit can use the electrogram informationto identify morphological characteristics (e.g., timings, amplitudes,shapes, etc.).

Specific features from an ECG sensor can include, but are not limitedto, RR interval/heart rate; P-wave detection (or a surrogate); Q-wavedetection (or a surrogate); intervals between any of the features of aPQRST waveform; heart rate variability (HRV); heart rate density indexof heart rate; AVNN (average of all NN intervals); SDNN (standarddeviation of all NN intervals)—which is a measure of long term heartrate variability (HRV); SDANN (standard deviation of the averages of NNintervals in all 5-minute segments of a 24-hour recording)—which is ameasure of long term HRV; SDNNIDX (mean of the standard deviations of NNintervals in all 5-minute segments of a 24-hour recording); RMSSD(square root of the mean of the squares of differences between adjacentNN intervals)—which is a measure of short term HRV); pNN50 (percentageof differences between adjacent NN intervals that are greater than 50ms); power spectrum of HRV signal to determine overall spectral densityin Very Low frequency (VLF) band, Low Frequency (LF) band, and HighFrequency (HF) band; ratios of either two of VLF, LF, and HF bands; QRScomplex amplitude or morphology, or surrogate thereof; match between ECGwaveform and optimal morphology template (the optimal template can bedefined and/or updated by a physician or internal algorithm based onmorphology during times of good therapy), the match can be quantifiedusing correlation between the two signals or can be quantified by when agiven signal leaves an interval around the mean defined by, for example,twice the standard deviation; PP interval; PR interval; QRS azimuth; QRSduration; ST segment; QRS-T angle; QT interval; or dimensions obtainedthrough dimensionality reduction of the entire waveform such asprincipal components analysis.

In some embodiments, a blood volume pulse (BVP) sensor can be used. Insome embodiments, the BVP sensor can be a photoplethysmography (PPG)sensor, and the pulsatile information (including timing, shape, andmorphology) can obtained by passing light through the neighboringartery. Aspects of PPG sensors are described in U.S. Pat. No. 8,494,606and U.S. Publ. Appl. No. 2017/0042435, the content of which is hereinincorporated by reference. In some embodiments, the BVP sensor canmeasure externally from a finger, wrist, ear, etc. In some embodiments,the BVP sensor can measure internally near an artery. In someembodiments, the BVP sensor is an electrical bioimpedance/impedancecardiography sensor, and the pulsatile information (including timing,shape, and morphology) is obtained by measuring change in impedanceacross artery as blood flow changes. In some embodiments, the BVP sensoris an accelerometer, and pulsatile information (including timing, shape,and morphology) is obtained by measuring changes in position as shape ofartery changes during blood flow. In some embodiments, the BVP sensor isa pressure sensor around or nearby the artery, and the pulsatileinformation (including timing, shape, and morphology) is directlymeasured from artery. In some embodiments, the BVP sensor is a pressuresensor inside the artery, and the pulsatile information (includingtiming, shape, and morphology) is directly measured within the artery.

Specific features from a BVP sensor can include match between a BVPwaveform and an optimal morphology template (the optimal template can bedefined and/or updated by a physician or internal algorithm based onmorphology during times of good therapy and a match can be quantifiedusing correlation between the two signals or by when a given signalleaves an interval around the mean defined by, for instance, twice thestandard deviation); systolic amplitude; diastolic amplitude; area underBVP waveform; pulse rate variability (calculated in any measure similarto HRV from ECG); pulse transit time; DC component of BVP waveform; ACcomponent of BVP waveform; dicrotic notch amplitude; time betweensystolic and diastolic peaks; or dimensions obtained throughdimensionality reduction of the entire waveform such as principalcomponents analysis.

In some embodiments, an electrodermal activity (EDA) sensor can be used.Aspects of electrodermal sensors are described in U.S. Publ. Appl. No.2017/0014043, the content of which is herein incorporated by reference.The surface electrode can measure skin conductance from, the hand(palmar surface), the foot (plantar surface), the wrist (such asincorporated into a wrist worn monitoring device), or an implanteddevice that is communicatively coupled to a conductive layer (tattoo)anywhere on the skin.

In some embodiments, a blood pressure sensor can be used. In variousembodiments, blood pressure can be derived from heart sounds signal, aBVP signal, a blood pressure cuff, or the like.

In some embodiments, a respiration sensor can be used. In variousembodiments, respiration can be sensed through contact based methods,through chest and abdominal movement detection, through acoustic basedmeasures, airflow monitoring, a muscle strain sensor, or impedance basedmeasures. Non-contact methods can also be used to detect respiration.

The respiration sensor can be an implantable sensor configured tomonitor subject chest expansion and contraction. In an example, therespiration sensor can be configured to provide information about asubject's tidal volume or minute ventilation. In some embodiments, therespiration sensor can be an acoustic sensor. The acoustic sensor can bean implantable transducer such as a microphone or accelerometer. Theacoustic sensor can be configured to receive acoustic vibrational energyfrom a subject, such as in the audible spectrum. In an example, aportion of the circuitry can be configured to receive information fromthe acoustic sensor and identify respiration information. In someembodiments, the respiration sensor can be a vibration sensor. Thevibration sensor can be an implantable transducer, such as anaccelerometer. The vibration sensor can be configured to receivevibrational energy from a patient and can be used to identifyrespiration information. In some embodiments, the respiration sensor canbe an impedance sensor configured to determine respiration data. Theimpedance sensor can include at least two electrodes disposed in thepatient's body and configured to detect electrical signals therein. Thedevice can be configured to receive electrical signal information fromthe impedance sensor to identify a detected or measured impedancebetween the two or more electrodes. In an example, a processor circuitcan be used to process the received impedance information to identifyrespiration data.

Specific features from a respiration sensor can include absolute HRVduring inspiration and expiration; ratio of HRV during inspiration toexpiration; absolute HR during inspiration and expiration; and change inHR over respiration cycle.

Pulmonary data can be used in some embodiments. Pulmonary data caninclude forced expiratory volume in 1 second (FEV1), forced vitalcapacity (FVC), FEV1/FVC, or various other lung function/spirometry testparameters. In some embodiments capnography can be used to gather dataherein. In various embodiments, data indicative of a change in thepulomonary condition can be used including lung sounds, trans-thoracicimpedance, vocal expression, and the like.

In some embodiments, an EEG sensor can be used. The EEG sensor can beembodied as electrodes in an EEG cap, EEG headsets, ear EEG devices,implanted subdermal wireless electrode(s) implant, implantedneuromodulation leads (e.g. occipital, trigeminal, deep brainstimulation leads), or other EEG measuring devices within sunglasses,hats, patches, etc.

In some embodiments, data from a sensor such as an electrogastrogram(EGG) can also be used. Aspects of electrogastrograms are described inU.S. Pat. No. 5,704,368, the content of which is herein incorporated byreference.

In some embodiments, an NIRS (near-infrared spectroscopy) sensor can beused. The NIRS sensor can be embodied as an individual NIRS optode, amulti-optode NIRS (e.g. measured using a cap, similar to EEG), asubdermal optode(s) implant. Aspects of NIRS sensors are described inU.S. Publ. Appl. No. 2014/0051956, the content of which is hereinincorporated by reference.

EEG/NIRS features can include dominant frequency, spectral power(relative and absolute), including total power as well as individualspectral power for specific brain wave frequencies (alpha, beta, theta,etc.); coherence, cross-coherence, spectral entropy, mutual information,and other correlation measures; changes (frequency shifting) in thedominant amplitude peak for relevant frequency bands, q-factor basedmetrics; and other EEG/NIRS-specific metrics.

In some embodiments, the sensor can be an activity or gait sensor. Forexample, the sensor can one or more of a 3-axis accelerometer, 3-axisgyroscope, and/or 3-axis magnetometer. In some embodiments, the sensorcan be an electromyography (EMG) sensor.

Baroreceptor reflex sensitivity (BRS) features can be calculated usingmeasures such as blood pressure; heart rate or inter-beat Interval(IBI); heart rate variability; change in heart rate—captured as a slopeof change or as a time interval for the parameter to reach X % of thepeak change; change in blood pressure—captured as a slope of change oras a time interval for the parameter to reach X % of the peak change.BRS can be classified based on the level of physical activity orexertion indicated by the activity and respiration sensors (e.g., mildactivity, moderate activity, or vigorous activity). BRS cancharacterized over a continuum of levels of physical activity orexertion indicated by the activity and respiration signals, for example,by vector magnitude units (in g) over a period of time, caloricexpenditure, distance traveled, or other activity or exertion measures,or a combination thereof.

Aspects from medical records can also be used as data herein. Examplesof such data include, but are not limited to, medication information,previous symptoms, previous diagnoses, previously obtained diagnostictest results, previous medical procedures performed on the patient, andthe like.

Data herein can include sleep data. Sleep data can include, but is notlimited to, average sleep duration, REM sleep cycles and durations,sleep quality, activity during sleep, sleep apnea incidents, breathingpatterns during sleep, waking episodes, morning waking time, and thelike.

In addition to other types of data described herein, in some embodimentsdemographic features from patient can be used, including but not limitedto, age, sex, geography, and/or ethnicity. Other types of data caninclude the time of day when measurements are taken.

In some embodiments, external environmental condition data can also beused. Environmental condition data can include, but is not limited to,humidity, external temperature, current weather, pollution level and thelike.

In some embodiments, data regarding the patient's use of, or irregularpatterns regarding, the Internet, social media, Internet searches, andthe like can be used.

Methods

Embodiments herein can include various methods. Exemplary methods caninclude any of the approaches and/or operations described herein. In anembodiment, a method of assessing the health status of a patient isincluded. The method can include evaluating the presence of volatileorganic compounds in a breath or gas sample of the patient to generatevolatile organic compound data. The volatile organic compound data canbe gathered using systems and devices such as those described herein.

In some cases, the volatile organic compound data can reflect the outputof a plurality of graphene sensors. The plurality of graphene sensorscan include sensors that are specific for different volatile organiccompounds. In some embodiments, the plurality of graphene sensors candetect the presence of at least 5, 10, 15, 20, 30, 40 or more differentvolatile organic compounds. In some embodiments, the number of differentvolatile organic compounds detected by the graphene sensors can be in arange wherein any of the forgoing numbers can serve as the upper orlower bound of the range provided that the upper bound is greater thanthe lower bound.

In some embodiments, one or more of the plurality of graphene sensorsare chosen as controls on the collected data regarding the patient'ssympathetic nervous activity. In some embodiments, the controlscorrelate with sympathetic nervous activity. In some embodiments, themethod can include generating a notification if the measured values ofthe controls do not match what would be expected for the measured valuesof sympathetic nervous activity.

The method can further include collecting data regarding the patient'ssympathetic nervous activity. In specific, the data regarding thepatient's sympathetic nervous activity can include changes in thepatient's sympathetic nervous activity and/or trends regarding the same.Sympathetic nervous activity can be gathered in either a clinical or anon-clinical environment. In some embodiments, data regarding thepatient's sympathetic nervous activity can be gathered using a wearabledevice and/or an implanted device. In some embodiments, the collecteddata regarding the patient's sympathetic nervous activity reflects abaseline level of sympathetic nervous activity and changes over thebaseline level of sympathetic nervous activity.

Many different types of data that reflect a patient's sympatheticnervous activity can be used. However, in some embodiments, the dataregarding the patient's sympathetic nervous activity can be selectedfrom the group consisting of heart rate variability (HRV), electrodermalactivity (EDA), blood pressure, respiratory rate, respiratory sinusarrhythmia (RSA), and baroreceptor sensitivity (BRS).

In some embodiments, data regarding the patient's sympathetic nervousactivity can be gathered over a period of time. In some embodiments,collecting data regarding the patient's sympathetic nervous activity isperformed over a time period of at least about 1 second, 5 seconds, 10seconds, 15 seconds, 30 seconds, 60 seconds, 5 minutes, 10 minutes, 20minutes, 30 minutes, 45 minutes, 60 minutes, 2 hours, 4 hours, 8 hours,12 hours, 24 hours, 2 days, 3 days, 4 days, 5 days, 10 days, 15 days, 20days, 30 days, 45 days, 60 days, 90 days, 120 days or more. In someembodiments, the data can be collected over a time period in a rangewherein any of the foregoing times can serve as the upper or lower boundof the range, provided that the upper bound in greater than the lowerbound. While not intending to be bound by theory, it is believed thatthe longer the duration, the more data will exist on the patient'sbaseline state and the better the ability to detect or predict anyexcursion from the baseline state.

Regardless of where the data is gathered, in many embodiments, the datacan be exchanged with other devices and/or other components of a system.In some embodiments, the volatile organic compound data from a breathsample of the patient is downloaded onto at least one of a wearabledevice and an implantable device from an external breath testing device.In some embodiments, collected data regarding the patient's sympatheticnervous activity is uploaded from a wearable device to clinicaldiagnostic device.

The method can further include combining the volatile organic compounddata with the collected data regarding the patient's sympathetic nervousactivity to form a combined data set. In some cases, all pieces of datain the combined data set can be weighted equally. In other cases, someof the pieces of data in the combined data set can be weighted moreheavily than others. In some embodiments, some pieces of data may simplyserve as a control.

It will be appreciated that although the combined data set frequentlyincludes volatile organic compound data and data regarding the patient'ssympathetic nervous state, still other types of data can be added intothe combined data set, such as other types of data described herein. Forexample, in some embodiments the method can also include collecting dataregarding the patient's functional status. The data regarding thepatient's functional status can be selected from the group consisting ofgait and accelerometry data. In some embodiments, the method can alsoinclude adding the data regarding the patient's functional status to thecombined data set.

The method can further include comparing the combined data set againstone or more previously determined patterns using a pattern matching orpattern recognition algorithm to determine the pattern that is the bestmatch, wherein the specific previously determined pattern that is thebest match indicates the health status of the patient.

By way of example, patterns amongst large sets of patient data may beoriginally identified through machine learning analysis or anothersimilar algorithmic technique. For example, a training set of dataincluding: 1.) information regarding volatile organic compounds for aset of patients, 2.) information regarding sympathetic nervous state oractivity for the same set of patents, 3.) information regarding specificdiagnoses or other health statuses for the same set of patients, and/or4.) other types of data described herein can be processed with a machinelearning algorithm or similar algorithmic technique in order to generateone or more patterns of volatile organic compounds, sympathetic nervousstate, and/or other data that correlate with certain diagnosis or healthstatuses.

Algorithms can be used to create new models using any of numerousmachine learning techniques, or apply the results of previouslycalculated models using these techniques, such as logistic regression;random forest, or an artificial neural network.

Many different pattern matching or pattern recognition algorithms can beused. By way of example, in some embodiments a least squares algorithmcan be used to identify a particular pre-determined pattern that acombined data set most closely matches.

In some embodiments, the patient can be prompted to take a breath or gastest (where the test could be performed either in a non-clinical settingsuch as their home or where such a prompt could cause them to come to aclinical setting to take the test).

In some embodiments, a pattern including such things as sleep patterns(e.g. wearable, implant or non-contact in-home sensor), physiologicaldata (autonomic tone measures), body weight (such as weightautomatically measured by a mat in the house), activity levels (e.g.mobile device, wearable, implant or non-contact in-home sensor), etc.can be assessed, such as using an algorithm, and if the results of thosefactors so indicate, then the system can inform the user that theyshould administer or get a breath or gas test to detect early signs ofheart failure decompensation. If a positive result, or data trends arebeyond a normal range for the individual patient, then the system caninform the patient to seek medical care for early intervention.

In some embodiments, a pattern including things such as sleep patterns,autonomic tone, respiratory rate, respiratory sounds, activity levels,etc., can be used to recommend to the user that they should administer abreath test (or come to a clinic to get a breath test) to detect earlysigns of a COPD exacerbation or repeat exacerbation. If a positiveresult, or data trends beyond normal range for the individual patient,seek medical care and/or use prescribed pharmaceutical (e.g.bronchodilators, corticosteroids, etc.) for early intervention.

Beyond, heart failure decompensation and COPD, such patterns and promptsto the patient to get a breath test can also be used for diabetesmanagement and inflammatory bowel diseases (also including dataregarding dietary intake, autonomic tone, etc. in the pattern) to detectearly signs of a flare-up.

It should be noted that, as used in this specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the content clearly dictates otherwise. Thus, for example,reference to a composition containing “a compound” includes a mixture oftwo or more compounds. It should also be noted that the term “or” isgenerally employed in its sense including “and/or” unless the contentclearly dictates otherwise.

It should also be noted that, as used in this specification and theappended claims, the phrase “configured” describes a system, apparatus,or other structure that is constructed or configured to perform aparticular task or adopt a particular configuration to. The phrase“configured” can be used interchangeably with other similar phrases suchas arranged and configured, constructed and arranged, constructed,manufactured and arranged, and the like. “Circuitry” can include bothhardwired circuitry for execution of particular operations as well asprocessors that are programmed to execute instructions to provide thesame functionality.

All publications and patent applications in this specification areindicative of the level of ordinary skill in the art to which thisspecification pertains. All publications and patent applications areherein incorporated by reference to the same extent as if eachindividual publication or patent application was specifically andindividually indicated by reference.

Aspects have been described with reference to various specific andpreferred embodiments and techniques. However, it should be understoodthat many variations and modifications may be made while remainingwithin the spirit and scope herein. As such, the embodiments describedherein are not intended to be exhaustive or to limit the scope to theprecise forms disclosed herein. Rather, the embodiments are chosen anddescribed so that others skilled in the art can appreciate andunderstand the principles and practices.

The invention claimed is:
 1. A method of assessing the health status ofa patient comprising: evaluating the presence of volatile organiccompounds in a breath or gas sample of the patient with a plurality ofgraphene sensors to generate volatile organic compound data, wherein theplurality of graphene sensors include sensors that are specific fordifferent volatile organic compounds; collecting data regarding thepatient's sympathetic nervous activity, the data comprising at least oneof heart rate variability (HRV), electrodermal activity (EDA),respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS);combining the volatile organic compound data with the collected dataregarding the patient's sympathetic nervous activity to form a combineddata set; and matching the combined data set against one or morepreviously determined data patterns using a pattern matching algorithmto determine the data pattern that is the best match, wherein thespecific previously determined data pattern that is the best matchindicates the health status of the patient.
 2. The method of claim 1,wherein the one or more previously determined data patterns are createdusing a machine learning process.
 3. The method of claim 1, furthercomprising collecting data regarding the patient's functional status,the data comprising gait data; and adding the data regarding thepatient's functional status to the combined data set.
 4. The method ofclaim 1, further comprising collecting data regarding the patient'sdemographic features; and adding the data regarding the patient'sdemographic features to the combined data set.
 5. The method of claim 1,wherein collecting the data regarding the patient's sympathetic nervousactivity is performed in a non-clinical setting and evaluating thepresence of the volatile organic compounds is performed in a clinicalsetting.
 6. The method of claim 1, wherein collecting the data regardingthe patient's sympathetic nervous activity is performed with a wearabledevice.
 7. The method of claim 1, wherein collecting the data regardingthe patient's sympathetic nervous activity is performed over a timeperiod of at least about 1 day.
 8. The method of claim 1, whereincollecting the data regarding the patient's sympathetic nervous activityis performed with an implanted device.
 9. The method of claim 1, whereinthe volatile organic compound data from the breath or gas sample of thepatient is downloaded from an external breath sensing system onto atleast one of a wearable device and an implantable device.
 10. The methodof claim 1, wherein the collected data regarding the patient'ssympathetic nervous activity is uploaded from a wearable device to aclinical diagnostic device.
 11. The method of claim 1, wherein one ormore of the plurality of graphene sensors are chosen as controls on thecollected data regarding the patient's sympathetic nervous activity. 12.The method of claim 11, wherein the controls correlate with sympatheticnervous activity.
 13. The method of claim 12, further comprisinggenerating a notification if the measured values of the controls do notmatch the measured values of sympathetic nervous activity.
 14. Themethod of claim 1, wherein the collected data regarding the patient'ssympathetic nervous activity reflects a baseline level of sympatheticnervous activity and changes over the baseline level of sympatheticnervous activity.
 15. The method of claim 1, wherein the plurality ofgraphene sensors can detect the presence of at least 10 differentvolatile organic compounds.
 16. A diagnostic health system comprising: acommunications circuit; a memory circuit; and a processor in electroniccommunication with the communication circuit and the memory circuit, theprocessor is configured to combine volatile organic compound data withcollected data regarding a patient's sympathetic nervous activity toform a combined data set, the collected data comprising at least one ofheart rate variability (HRV), electrodermal activity (EDA), respiratorysinus arrhythmia (RSA), and baroreceptor sensitivity (BRS); and matchthe combined data set against one or more previously determined datapatterns using a pattern matching algorithm to determine a pattern thatis the best match, wherein the specific previously determined patternthat is the best match indicates the health status of the patient; andreport the health status of the patient based on the best pattern match.17. The diagnostic health system of claim 16, wherein the diagnostichealth system is a wearable device and the volatile organic compounddata is downloaded onto the wearable device from another device.
 18. Thediagnostic health system of claim 16, wherein the diagnostic healthsystem is disposed in a clinical environment and collected dataregarding a patient's sympathetic nervous activity is uploaded to thediagnostic health system from a wearable device.
 19. A diagnostic healthsystem comprising: a patient-specific device selected from the groupconsisting of a wearable device and an implanted device; and an externalbreath sensing system; and a processor receiving data from thepatient-specific device and the external breath sensing system; whereinthe patient-specific device collects data regarding a patient'ssympathetic nervous activity, the data comprising at least one of heartrate variability (HRV), electrodermal activity (EDA), respiratory sinusarrhythmia (RSA), and baroreceptor sensitivity (BRS); wherein theexternal breath sensing system collects data regarding the presence ofvolatile organic compounds in a breath or gas sample of the patient; andwherein the processor is configured to combine the volatile organiccompound data with the patient's sympathetic nervous activity data toform a combined data set; and match the combined data set against one ormore previously determined data patterns using a pattern matchingalgorithm to determine a pattern that is the best match, wherein thespecific previously determined pattern that is the best match indicatesthe health status of the patient; and report the health status of thepatient based on the best pattern match.